Exploring the evolution of artificial intelligence in pathology: a bibliometric and network analysis

Aims: Artificial intelligence (AI) has emerged as a transformative force in pathology, significantly influencing diagnostic accuracy, workflow efficiency, and digital pathology integration. Despite the rapid growth in AI-related pathology research, a comprehensive analysis of publication trends, key contributors, and scientific impact remains limited. This study aims to provide a bibliometric and network analysis of AI applications in pathology, mapping research trends, citation networks, institutional collaborations, and emerging thematic clusters. Methods: A bibliometric analysis was conducted using data from the Web of Science Core Collection, covering studies published between 2007 and 2024. Research trends, citation distributions, keyword co-occurrences, and collaboration networks were analyzed using VOSviewer. Descriptive statistics and network visualization techniques were applied to assess publication growth, author collaborations, and journal impact. Results: The findings are consistent with other studies showing a more than proportionate increase in AI-based research in pathology since 2018, especially AI related pathology research is on a significant rise focusing on laboratory investigation, modern pathology and journal of pathology as the primary high impact journals. Important research centers like the University of Pittsburgh, Radboud Universiteit, and the Cleveland Clinic have made significant advancements in AI based pathology which have and will continue to make a significant impact within this area. The key words used most frequently were “AI”, “digital pathology”, “deep learning”, and “machine learning” which corroborate the centrality of AI in pathology. Conclusion: AI does have a major contribution towards transforming pathology by aiding in providing quick and efficient diagnosis. Nonetheless, issues around the standardization of data, the black box nature of algorithms, and the regulation of data raise serious challenges towards achieving successful clinical incorporation of AI. The focus of future work should center around standardization of validation protocols, inter-disciplinarity, and ethical issues in order to ensure the dependable implementation of AI enabled solutions in pathology.